材料聲阻抗為材料重要聲學性質之一，使用阻抗管量測為常見的量測方式，卻僅限於一維平面波的條件之下，為了可以擺脫此限制，本研究通過有限元素分析軟體Abaqus以及多層倒傳遞類神經網路，進行材料聲阻抗的預測與分析。本文研究目的為預測成大拖航水槽之槽壁聲阻抗，一開始考慮到模擬數據的參考價值，故透過模擬充水阻抗管，進行確定Abaqus分析的可靠性，在確定理論計算與結果成立後，由Abaqus模擬分析數據並進行資料庫的建置，訓練資料蒐集完成後，便建構預測材料聲阻抗之多層倒傳遞類神經網路（Multi-Layer Perceptrons，簡稱MLP），將水槽聲場聲壓大小及相位變化做為特徵進行訓練，找出與材料聲阻抗的關係，訓練完成的模型則可以通過量測水槽聲場聲壓大小及相位，來預測材料的聲阻抗。

Acoustic impedance is one of the important acoustic properties of materials. It is a common method to measure acoustic impedance by impedance tube method, but it is limited to one-dimensional plane wave. In order to overcome of the limitation, this research applies finite element analysis software and neural network to inverse and analyze the acoustic impedance of underwater materials. The purpose of this study is to predict the acoustic impedance of the towing tank walls.
At the beginning, considering the reliability of the simulation data, the reliability of the simulation data was determined through the simulation and theory of the water-filled impedance tube. After the reliability of the simulation data is determined, the simulation analysis of the towing tank is started, and the database is established.
Sound pressure of towing tank of numerical simulation are used as features for training to find out the relationship with the acoustic impedance of the material and construct a Multi-Layer Perceptrons(MLP) networks for predicting the acoustic impedance of materials. The trained model can predict the acoustic impedance of the material by measuring the sound pressure.

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